Browsing by Author "Cheng, M."

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    Epigenetic Modulations and Lineage Plasticity in Advanced Prostate Cancer
    (Elsevier, 2020-04) Ge, R.; Wang, Z.; Montironi, R.; Jiang, Z.; Cheng, M.; Santoni, M.; Huang, K.; Massari, F.; Lu, X.; Cimadamore, A.; Lopez-Beltran, A.; Cheng, L.; Pathology and Laboratory Medicine, School of Medicine
    Prostate cancer is the most common cancer and second leading cause of cancer-related death in American men. Antiandrogen therapies are part of the standard of therapeutic regimen for advanced or metastatic prostate cancers; however, patients who receive these treatments are more likely to develop castration-resistant prostate cancer (CRPC) or neuroendocrine prostate cancer (NEPC). In the development of CRPC or NEPC, numerous genetic signaling pathways have been under preclinical investigations and in clinical trials. Accumulated evidence shows that DNA methylation, chromatin integrity, and accessibility for transcriptional regulation still play key roles in prostate cancer initiation and progression. Better understanding of how epigenetic change regulates the progression of prostate cancer and the interaction between epigenetic and genetic modulators driving NEPC may help develop a better risk stratification and more effective treatment regimens for prostate cancer patients.
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    Polyphenic risk score shows robust predictive ability for long-term future suicidality
    (Springer, 2022) Cheng, M.; Roseberry, K.; Choi, Y.; Quast, L.; Gaines, M.; Sandusky, G.; Kline, J.A.; Bogdan, P.; Niculescu, A.B.; Psychiatry, School of Medicine
    Suicides are preventable tragedies, if risk factors are tracked and mitigated. We had previously developed a new quantitative suicidality risk assessment instrument (Convergent Functional Information for Suicidality, CFI-S), which is in essence a simple polyphenic risk score, and deployed it in a busy urban hospital Emergency Department, in a naturalistic cohort of consecutive patients. We report a four years follow-up of that population (n = 482). Overall, the single administration of the CFI-S was significantly predictive of suicidality over the ensuing 4 years (occurrence- ROC AUC 80%, severity- Pearson correlation 0.44, imminence-Cox regression Hazard Ratio 1.33). The best predictive single phenes (phenotypic items) were feeling useless (not needed), a past history of suicidality, and social isolation. We next used machine learning approaches to enhance the predictive ability of CFI-S. We divided the population into a discovery cohort (n = 255) and testing cohort (n = 227), and developed a deep neural network algorithm that showed increased accuracy for predicting risk of future suicidality (increasing the ROC AUC from 80 to 90%), as well as a similarity network classifier for visualizing patient’s risk. We propose that the widespread use of CFI-S for screening purposes, with or without machine learning enhancements, can boost suicidality prevention efforts. This study also identified as top risk factors for suicidality addressable social determinants.